A fundamental problem for Bayesian mixture model analysis is label switching, which
occurs due to the non-identifiability of the mixture components under symmetric priors.
We propose two labelling methods to solve this problem. The first method, denoted by
PM(ALG), is based on the posterior modes and an ascending algorithm generically denoted
ALG. We use each Markov chain Monte Carlo (MCMC) sample as the starting point in an
ascending algorithm, and label the sample based on the mode of the posterior to which it converges. Our natural assumption here is that the samples converged to the same mode
should have the same labels. The PM(ALG) labelling method has some computational
advantages over other popular labelling methods. Additionally, it automatically matches the “ideal” labels in the highest posterior density credible regions. The second method does labelling by maximizing the normal likelihood of the labelled Gibbs samples. Using a Monte Carlo simulation study and a real data set, we demonstrate the success of our new methods in dealing with the label switching problem.

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http://www.tandfonline.com/doi/abs/10.1198/jasa.2009.0237

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This is an electronic version of an article published in Journal of the American Statistical Association, 104(486), 758-767. Journal of the American Statistical Association is available online at: http://www.tandfonline.com/doi/abs/10.1198/jasa.2009.0237